Foreword

Oasis between Samarkand and Buchara. Source: Tobias Siegfried, hydrosolutions GmbH.

This is a online book about applied hydrological modeling. It is geared towards students and young professionals in Central Asia who are interested in learning modern modeling approaches. The book teaches by examples and uses two catchments from the Syr Darya and Amu Darya river basins as case studies. While the presented case studies are exclusively from Central Asia, the methods demonstrated can be applied elsewhere.

The book is accompanied by a Study Exercise Pack that encompasses 7 Central Asian catchments which can be used by students for learning and applying their skill to real-world examples in the region. The Pack can accessed and downloaded here. Furthermore, a dedicated R Package has been developed which implements many of the data analyses and processing steps shown in this book. More information can be found on Github, where the package is maintained. R which is a programming language widely used in data analysis and mining (R Core Team 2013).

The course is structured in the following way. First, key hydro-climatological characteristics of the region are presented. This Section draws heavily on Victor Shults’ “Rivers of Middle Asia” and presents relevant materials from this famous book in a modern way. Two important basins are further highlighted as in-depth case studies, i.e. the Gunt River in the Amu Darya catchment and the Chirchik river basin. The analyses of these catchments draws on available data from the Central Asian Hydrometeorological Services and on global public hydro-climatological as well as land cover datasets.

A large Chapter on data covers in-situ station data, geospatial data, climate reanalysis and climate projection data. Workflows are presented for the Gunt river basin how to prepare all required data for transient hydrological model at hourly time scales.

Three different types of hydrological modeling approaches are demonstrated and discussed in greater depth. First, long-term water balance modeling using the Budyko framework is introduced to discuss key determinants of the partitioning of precipitation into runoff and evaporation over climatological time scales.

Second, a more detailed modeling approach using semi-distributed, conceptual hydrologic-hydraulic modeling will be used to demonstrate the implementation, calibration and validation of such type of models. Again, working by example, the free modeling software RS MINERVE is used for this purpose.

Third and finally, empirical modeling will be introduced for forecasting. These types of models are currently utilized in the Hydrometeorological Agencies of the region for forecasting discharge at different lead times, ranging from one day ahead up to seasonal forecasts. These models use long term time series to learn relationships between past, observed quantities and future system responses.

With everything that is presented, the focus is on the use of open source and free software. For data preparation and analysis as well as for water balance and empirical modeling, R and RStudio are utilized (R Core Team 2013; Team’ 2020). For the processing of geographic data, workflows in QGIS are demonstrated (QGIS Development Team 2021). For hydrological-hydraulic modeling, the free RS MINERVE is utilized which is a environment for the modeling of free surface runoff flow formation and propagation (Foehn et al. 2020; Garcia Hernandez et al. 2020). The reader is expected to have a basic understanding about R and QGIS and how to use these software for data analysis and processing.

The R code to produce figures, conduct analyses as well carry out empirical modeling is deliberately provided throughout the text and embedded there. While this makes the text heavier than necessary, it gives the local target group to reuse code and directly reproduce results while working on their own problems. An attempt has been made to make everything as reproducible as possible.

Feedback is welcome!